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Economic Messages in Prescription Drug Advertisements in Medical Journals

2002· article· en· W2020913875 on OpenAlexaff
Peter J. Neumann, Vijay R. Ramakrishnan, Kate Stewart, Chaim M. Bell

Bibliographic record

VenueMedical Care · 2002
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrescription drugMedical prescriptionAdvertisingDrugBusinessMedicineFamily medicinePharmacology

Abstract

fetched live from OpenAlex

BACKGROUND: The extent to which pharmaceutical companies promote the economic advantages of their products in advertisements in medical journals, and whether such claims are supported by evidence, has not been quantified. Our objectives were to examine how often prescription drug advertisements in leading medical journals contain economic messages, and to determine the types of promotional claims made and whether supporting evidence is provided. METHODS: All prescription drug advertisements appearing in six leading general medical and specialty journals in 3 selected months annually from 1990 to 1999 were reviewed. Using a standard data collection form, two reviewers examined each ad for economic content-including mention of the drug's price, value, cost saving, or cost-effectiveness. RESULTS: Economic messages appeared in 237 (11.1%) of the 2144 advertisements examined. Proportion of ads with economic content has increased over time (P = 0.003). Most frequently, economic ads contained statements that drugs were "less expensive" or "cost less" than alternative treatments (50.6% of economic ads). Supporting evidence for economic claims was clearly reported in 63.7% of cases, and typically referred to published drug prices rather than more detailed economic analysis. Ads for calcium channel blocking agents and ACE inhibitors frequently contained economic messages. CONCLUSIONS: Economic messages about prescription drugs are used in advertisements in leading medical journals and their frequency may be rising. Physicians should be aware of this phenomenon, and its potential impact on their prescribing decisions. More scrutiny of the supporting evidence underlying economic claims by the medical community and regulators may be needed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.344
GPT teacher head0.555
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2002
Admission routes1
Has abstractyes

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